Experienced entropy drives choice behavior in a boring decision-making task

Boredom has been defined as an aversive mental state that is induced by the disability to engage in satisfying activity, most often experienced in monotonous environments. However, current understanding of the situational factors inducing boredom and driving subsequent behavior remains incomplete. Here, we introduce a two-alternative forced-choice task coupled with sensory stimulation of different degrees of monotony. We find that human subjects develop a bias in decision-making, avoiding the more monotonous alternative that is correlated with self-reported state boredom. This finding was replicated in independent laboratory and online experiments and proved to be specific for the induction of boredom rather than curiosity. Furthermore, using theoretical modeling we show that the entropy in the sequence of individually experienced stimuli, a measure of information gain, serves as a major determinant to predict choice behavior in the task. With this, we underline the relevance of boredom for driving behavioral responses that ensure a lasting stream of information to the brain.


Supplementary Figures
Supplementary Figure 1 -Choice behavior of three exemplary subjects from Experiment Ia and b in all BCT conditions: The cumulative amount of choices for either alternative is plotted for each trial and the color indicates the respective task condition. Different subjects show a different magnitude of their boredom bias (e.g. compare Subj. 1 and Subj 2 in Mon-Var Auditory) and also vary in choice behavior from visual to the auditory task cycles (e.g. compare Subj. 1 Mon-Var Auditory and Mon-Var Visual). In the control conditions, subjects either switch regularly between alternatives (e.g. Subj. 3 in Mon-Mon Visual), or show an idiosyncratic bias for one of the equivalent alternatives (e.g. Subj. 3 in Mon-Mon Auditory, in this case with an idiosyncratic bias for the left alternative).
Supplementary Figure 2 -Subjects show an idiosyncratic bias for one alternative in the symmetric BCT cycles that reduces variance within the behavior in different BCT cycles: (A) Boxplots of the standard deviation of each individual's raw boredom biases for the alternative located at the right side of the screen in the four control cycles from the laboratory Experiments Ia and b (n = 102 participants, each with monotonous vs. monotonous and variable vs variable task cycles in visual and auditory modality). Individuals show less variance in their bias compared to randomly sampling four bias scores across individuals. This hints towards a systematic idiosyncratic bias on the level of single subjects. Both conditions show a significant statistical difference (Wilcoxon ranked sum test with ***: p < 0.001). (B) Equivalent plot for the raw boredom bias in the online Experiment Ic (n = 40 participants, each with one monotonous vs. monotonous and variable vs variable task cycle in visual modality), where the finding of an individual idiosyncratic bias is confirmed.

Supplementary Figure 3 -Comparison of laboratory and online experiments:
Comparing the extent of the boredom bias in the BCT under the controlled conditions of a laboratory (Experiments Ia and b) against less controlled online conditions (Experiment Ic), we found that the bias was less strongly pronounced in the online environment ( Figure 2C). We hypothesized that this difference may be produced by less task adherence, potentially due to a more distracting environment when conducting the experiment online. Therefore, for each individual we computed the average inter-trial latency as well as the average amount of switches in one BCT cycle, and compared these behavioral metrics between the laboratory experiments and the online experiment. We operationalize these behavioral outcomes as indirect representatives for adherence and attention to the BCT. (A) Boxplots of average response latencies from the laboratory studies (Experiment Ia and b) and online study (Experiment Ic). Online subjects show longer response latencies compared to the subjects from the laboratory study (median ± SD for individuals' average inter-trial latency: laboratory Experiments Ia+b: 1.59s ± 0.21s, n = 102 participants; online Experiment Ic: 1.83s ± 0.62s, n = 40 participants; Wilcoxon ranked sum test with ***: p < 0.001). The stimulus presentation time was set to 1 s, which explains the minimal values. (B) Boxplots of the average amount of switches per BCT cycle. Online subjects show significantly more switching between alternatives compared to the subjects from the laboratory study (median ± SD for individuals' average amount of switching: laboratory Experiments Ia+b: 79.08 ± 60.55, n = 102 participants; online Experiment Ic: 110.67 ± 96.62, n = 40 participants; Wilcoxon ranked sum test with *: p = 0.049). Both findings together indicate that subjects under online conditions are less adherent to the task (reflected by increased inter-trial intervals) and generally show a more alternating behavior (reflected by more frequent switching).

Supplementary Figure 4 -Raw boredom bias profiles and adaptations across experiments:
In Experiment II each subject completed 13 cycles of the BCT with different library sizes of visual stimuli. Each panel presents the average raw boredom bias over the trials of each task cycle (n = 148 participants). The raw boredom bias of each individual is computed in a bin of 15 trials that is then shifted stepwise until the end of the task (first bin: trial 1-15, last bin: trial 86-100). The average adaptation curves of the boredom bias qualitatively match the adaptation curves from Experiment Ia-c (see Figure 2B) with an initial increase of the boredom bias followed by a stable plateau phase. The magnitude of the maximal boredom bias increases as the difference between the two stimulus libraries becomes larger. Despite the different length of the BCT cycles in Experiment II with only 100 trials, the similar adaptation curves in Experiment II support a general comparability with the previous Experiments Ia-c with 300 trials.

Supplementary Figure 5 -Schematic example of entropy computation:
The table illustrates the computation of empirical entropy for the first 5 trials of an exemplary 4:1 stimuli BCT condition. The red frame marks the alternative which is chosen in each trial. In addition, the stimuli of the current trial and past trials are presented. Note that in this example not all possible stimuli from the libraries are sampled. Entropy for each alternative is computed on each trial as a fraction of the total entropy provided by the stimuli of both alternatives. If one alternative is never chosen, its entropy is set to zero. To compare the state of entropy between both alternatives we furthermore computed the difference in entropy for each trial.

Supplementary Figure 6 -Empirical entropy over trials in the different BCT cycles of Experiment II: (A)
Average empirical entropy for the two alternatives, computed over all stimuli that were experienced at the respective alternative up to the current trial (n = 148 participants). The vertical bars indicate the standard error of the mean. Empirical entropy of each alternative is computed for each trial and therefore evolves over the duration of the task cycles. (B) Average entropy difference between the two alternatives in each trial of the task cycles in Experiment II (n = 148 participants). The difference in empirical entropy depends on the difference between the stimulus libraries and is largest for the 64:1 condition. The vertical bars indicate the standard error of the mean.

Supplementary Table 1 -Sociodemographic characteristics of participants from Experiments Ia+b and II:
A pooled analysis of all participants from the three laboratory experiments (Experiment Ia+b and II) shows an average age of 23 years and a mean BMI of 22.7 kg/m2 corresponding to normal average weight. The majority of sampled individuals is female (72.0%), has a Caucasian background (94.8%) and stems from a small hometown with less than 10,000 inhabitants (43.6%). All participants are enrolled students from the Johannes-Gutenberg University Mainz, where the most frequent fields of study covered economics (23.6%), teaching post (10.8%) and law (8.8%). None of the participants suffered from active mental disorders, however a substantial fraction reported diagnosed mental disorders in the close family (30.0%). All in all, these results indicate a homogenous sample of young and healthy adults with a higher-than-average education. Supplementary Table 3 -Correlations of adjusted boredom bias and working memory capacity: Spearman correlations between the adjusted boredom bias and the working memory task scores (digit span backwards task) for the subjects of Experiment II that completed the working memory task (n = 72 participants). Due to the plateau level of the boredom bias for alternatives with stimulus libraries larger than 8 stimuli (see Figure 4B), we hypothesized that working memory capacity, which is typically reported to cover around 7 chunks of information, could moderate the amplitude of the boredom bias in the BCT. Therefore, we tested the correlation of working memory task scores and the adjusted boredom bias of monotony avoidance in all 13 task cycles of Experiment II. We did not observe a significant relationship for any of the task cycles, indicating that differences in working memory capacity are not able to explain the differences in the magnitudes of adjusted boredom bias. Note, for the symmetrical conditions 1:1 and 64:64 by construction no correlation would have been expected.
Translation of R. Farmer & N. D. Sundberg, 1986 Digit span backwards task of the study

English translation of the German task version that was used in the study
Introduction: "The following task requires you to memorize some sequences of numbers and write them down in reverse order to the paper in front of you. While listening to the sequence of digits you are not allowed to use any tools or notations. Please write down the sequences in reverse order, meaning that the last named digit should be noted at the beginning of your answer. The difficulty will increase from sequence to sequence.
First, you are presented two example sequences, before moving to the task condition." Test sequence I:

(Stepwise presentation of sequences with 1s between each digit)
Sequence I: